Fei Shuang

672 total citations
45 papers, 499 citations indexed

About

Fei Shuang is a scholar working on Mechanical Engineering, Materials Chemistry and Mechanics of Materials. According to data from OpenAlex, Fei Shuang has authored 45 papers receiving a total of 499 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Mechanical Engineering, 18 papers in Materials Chemistry and 8 papers in Mechanics of Materials. Recurrent topics in Fei Shuang's work include Microstructure and mechanical properties (10 papers), Metal and Thin Film Mechanics (6 papers) and Advanced machining processes and optimization (5 papers). Fei Shuang is often cited by papers focused on Microstructure and mechanical properties (10 papers), Metal and Thin Film Mechanics (6 papers) and Advanced machining processes and optimization (5 papers). Fei Shuang collaborates with scholars based in China, United States and Netherlands. Fei Shuang's co-authors include Katerina E. Aifantis, Katerina E. Aifantis, W. F. Mader, Xiangyu Chen, Guocheng Li, Zhaohe Dai, Wei Ma, Yilong Bai, Pan Xiao and Poulumi Dey and has published in prestigious journals such as Chemical Reviews, Nature Communications and Acta Materialia.

In The Last Decade

Fei Shuang

38 papers receiving 487 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Fei Shuang China 12 315 250 104 69 54 45 499
Ye Sang China 8 166 0.5× 122 0.5× 73 0.7× 143 2.1× 18 0.3× 15 379
Mark Wilson United States 11 217 0.7× 113 0.5× 45 0.4× 132 1.9× 32 0.6× 34 465
Gang Xiao China 13 346 1.1× 340 1.4× 25 0.2× 139 2.0× 81 1.5× 35 579
Tanner Kirk United States 11 371 1.2× 189 0.8× 55 0.5× 44 0.6× 15 0.3× 17 545
Yuanlong Li China 11 85 0.3× 111 0.4× 108 1.0× 25 0.4× 39 0.7× 43 426
Bohua Sun China 11 109 0.3× 113 0.5× 55 0.5× 82 1.2× 45 0.8× 51 423
Xiaoguang Zhou China 12 269 0.9× 187 0.7× 29 0.3× 180 2.6× 37 0.7× 42 461

Countries citing papers authored by Fei Shuang

Since Specialization
Citations

This map shows the geographic impact of Fei Shuang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fei Shuang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fei Shuang more than expected).

Fields of papers citing papers by Fei Shuang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fei Shuang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fei Shuang. The network helps show where Fei Shuang may publish in the future.

Co-authorship network of co-authors of Fei Shuang

This figure shows the co-authorship network connecting the top 25 collaborators of Fei Shuang. A scholar is included among the top collaborators of Fei Shuang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fei Shuang. Fei Shuang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ji, Yanzhou, Mahdieh Safyari, Chenyang Yao, et al.. (2025). Tailoring precipitates for enhanced hydrogen trapping in aluminum alloys. Nature Communications. 17(1). 279–279. 1 indexed citations
2.
Rahbari, Ahmadreza, Fei Shuang, Panagiotis Krokidas, et al.. (2025). Molecular Simulation of Hydrogen Systems: From Properties and Methods to Applications and Future Directions. Chemical Reviews. 125(24). 11878–12029.
3.
4.
Shuang, Fei, Yucheng Ji, Luca Laurenti, & Poulumi Dey. (2025). Size-dependent strength superiority in multi-principal element alloys versus constituent metals: Insights from machine-learning atomistic simulations. International Journal of Plasticity. 188. 104308–104308. 7 indexed citations
5.
Shuang, Fei, et al.. (2025). Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys. Machine Learning Science and Technology. 6(3). 30501–30501. 4 indexed citations
6.
Shuang, Fei, Yucheng Ji, Chaofang Dong, et al.. (2025). Decoding the hidden dynamics of super-Arrhenius hydrogen diffusion in multi-principal element alloys via machine learning. Acta Materialia. 289. 120924–120924. 4 indexed citations
7.
Gao, Xiang, Ming Shao, Bin Ni, et al.. (2025). Characterization of gut microbiota and metabolites in renal transplant recipients during COVID-19 and prediction of one-year allograft function. Journal of Translational Medicine. 23(1). 420–420. 1 indexed citations
8.
Sun, Li, Fei Shuang, Hao Chen, et al.. (2024). Long-term outcomes in rapamycin on renal allograft function: a 30-year follow-up from a single-center experience. BMC Nephrology. 25(1). 311–311.
9.
Shuang, Fei, et al.. (2024). Revealing the effect of inverse dislocation pileups on the mechanical properties of multi-principal element alloys. Journal of Material Science and Technology. 190. 155–171. 7 indexed citations
10.
Shuang, Fei, Luca Laurenti, & Poulumi Dey. (2024). Standard deviation in maximum restoring force controls the intrinsic strength of face-centered cubic multi-principal element alloys. Acta Materialia. 282. 120508–120508. 2 indexed citations
11.
Shuang, Fei, Rigelesaiyin Ji, Liming Xiong, & Wei Gao. (2023). Effect of periodic image interactions on kink pair activation of screw dislocation. Computational Materials Science. 228. 112369–112369.
12.
Shuang, Fei, Bo Wang, & Katerina E. Aifantis. (2023). Revealing multiple strengthening transitions in crystalline-amorphous nanolaminates through molecular dynamics. Materials Today Communications. 35. 105675–105675. 6 indexed citations
13.
Chen, Congcong, Dong Zhang, Zijie Wang, et al.. (2023). Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms. Frontiers in Genetics. 14. 1276963–1276963. 3 indexed citations
14.
Sun, Li, Zhengkai Huang, Fei Shuang, et al.. (2023). Vascular calcification progression and its association with mineral and bone disorder in kidney transplant recipients. Renal Failure. 45(2). 2276382–2276382. 2 indexed citations
15.
Shuang, Fei, et al.. (2023). Distinct nucleation and propagation of prismatic dislocation loop arrays in Ni and medium-entropy CrCoNi alloy: Insights from molecular dynamics simulations. Materials Today Communications. 36. 106791–106791. 3 indexed citations
16.
Shuang, Fei, Zhaohe Dai, & Katerina E. Aifantis. (2021). Strengthening in Metal/Graphene Composites: Capturing the Transition from Interface to Precipitate Hardening. ACS Applied Materials & Interfaces. 13(22). 26610–26620. 43 indexed citations
17.
Shuang, Fei & Katerina E. Aifantis. (2021). A First Molecular Dynamics Study for Modeling the Microstructure and Mechanical Behavior of Si Nanopillars during Lithiation. ACS Applied Materials & Interfaces. 13(18). 21310–21319. 15 indexed citations
18.
Shuang, Fei & Katerina E. Aifantis. (2020). Dislocation-graphene interactions in Cu/graphene composites and the effect of boundary conditions: a molecular dynamics study. Carbon. 172. 50–70. 75 indexed citations
19.
20.
Shuang, Fei & Katerina E. Aifantis. (2020). Modelling dislocation-graphene interactions in a BCC Fe matrix by molecular dynamics simulations and gradient plasticity theory. Applied Surface Science. 535. 147602–147602. 44 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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